Measuring the Effect of Waiting Time on Customer Purchases
Andrs Musalem Duke University
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Agenda Background My research Measuring the effect of waiting
time on customer purchases
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Background: Santiago, Chile Ph.D., Wharton Ind. Engineering
MBA, U. of Chile
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Teaching Interests: Market Research (U. Chile) Pricing
(Wharton) Marketing Management (WEMBA, CCMBA, MEM) GATE: Global
academic travel experience (Daytime MBA) South America Product
Management (WEMBA, CCMBA) Marketing Practicum (Daytime MBA):
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My research: Quantitative Marketing Mathematical models to
study: How consumers react to coupon promotions? Implications for
targeting How consumers react to out of stocks? Implications for
inventory planning How consumers react to waiting time?
Implications for customer service How to estimate demand for
products not yet introduced in a market? Implications for
assortment/product line decisions How should firms make efforts to
attract or retain customers? How should firms manage customer
expectations? underpromise and overdeliver? Data driven Game
Theory
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Measuring the Effect of Waiting Time on Customer Purchases
Andrs Musalem Duke University Joint work with Marcelo Olivares,
Yina Lu (Decisions Risk and Operations, Columbia Business School),
and Ariel Schilkrut (SCOPIX).
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RETAIL DECISIONS & INFORMATION Point of Sales Data Loyalty
Card / Customer Panel Data Competitive Information (IRI, Nielsen)
Cost data (wholesale prices, accounting) Customer Experience,
Service Assortment Pricing Promotions Lack of objective data
Surveys: Subjective measures Sample selection 8
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Operations Management Literature Research usually focuses on
managing resources to attain a customer service level Staff
required so that 90% of the customers wait less than 1 minute
Number of cashiers open so that less than 4 customers are waiting
in line. Inventory needed to attain a 95% demand fill rate. How
would you choose an appropriate level of service? Trade-off:
operating costs vs service levels Link between service levels and
customer purchase behavior 9 Research Goal
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Real-Time Store Operational Data: Number of Customers in Line
Snapshots every 30 minutes (6 months) Image recognition to
identify: number of people waiting number of servers + Loyalty card
data UPCs purchased prices paid Time stamp 10
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Modeling Customer Choice 11 Require waiting (W) No waiting
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Modeling Customer Choice 12 Require waiting (W) No waiting
Waiting cost for products in W Consumption rate &
inventoryPrice sensitivity consumer product visit Seasonality
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Matching Operational Data with Customer Transactions Issue: do
not know what the queue looked like (Q,E) when a customer visited
the deli section Use marketing and operations management tools to
model the evolution of the queue between snapshots (e.g., 4:45 and
5:15) : Choice Models: how likely is a customer to join the line if
Q customers are waiting? Queuing theory: how many customers will
remain in the queue by the time a new customer arrives? 13
4:154:455:155:45 ts: cashier time stamp Q L2(t ), E L2(t ) Q L(t ),
E L(t ) Q F(t ), E F(t ) ts Queue length Number of employees
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RESULTS 14
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Results: What drives purchases? Customer behavior is better
predicted by queue length (Q) than expected waiting time (W, which
is proportional to Q/E) 15
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Question: Consider two hypothetical scenarios: What if we
double the number of employees behind the counter? What if the
length of the line is reduced from 10 to 5 customers? Both half the
expected waiting time, but which one would have a stronger impact
on customer purchase behavior? Whats the implication? 16
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17 > Single line checkout for faster shopping
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Managerial Implications: Combine or Split Queues? Pooled
system: single queue with c servers Split system: c parallel single
server queues, customers join the shortest queue (JSQ) 18
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Managerial Implications: Combine or Split Queues? Pooled
system: single queue with c servers Split system: c parallel single
server queues, customers join the shortest queue (JSQ) 19
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20 Pooled system is more efficient in terms of average waiting
time In split system, individual queues are shorter => If
customers react to length of queue, this can help to reduce lost
sales (by as much as 30%) Managerial Implications: Combine or Split
Queues? congestion
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Estimated Parameters 21 Effect is non-linear Increase from Q=5
to 10 customers in line => equivalent to 1.7% price increase
Increase from Q=10 to 15 customers in line => equivalent to 5.5%
price increase Negative correlation between price & waiting
sensitivity Pre-packaged products dont help much. Attract only 7%
of deli lost sales when Q=5 -> Q=10 Effect is non-linear
Increase from Q=5 to 10 customers in line => equivalent to 1.7%
price increase Increase from Q=10 to 15 customers in line =>
equivalent to 5.5% price increase Negative correlation between
price & waiting sensitivity Pre-packaged products dont help
much. Attract only 7% of deli lost sales when Q=5 -> Q=10
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Waiting & Price Sensitivity 22
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Waiting & Price Sensitivity 23
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Managerial Implications: Category Pricing Example: Two products
H and L with different qualities and prices: p H > p L Customers
sensitive to price are insensitive to waiting and vice versa. What
if we offer a discount on the price of the L product? 24
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Congestion & Demand Externalities 25 $$$ $ $ $$ $$$ $ Price
Discount on Product L $
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Managerial Implications: Category Pricing Example: Two products
H and L with different prices: p H > p L Customers sensitive to
price are insensitive to waiting and vice versa. What if we offer a
discount on the price of the L product? If price and waiting
sensitivity are negatively correlated, a significant fraction of H
customers may decide not to purchase 26 Correlation between price
and waiting sensitivity -0.9 -0.500.50.9 Waiting None---0.04--
Sensitivity Medium-0.34-0.23-0.12-0.05-0.01 Heterogeneity
High-0.74-0.45-0.21-0.07-0.01 Cross-price elasticity of demand: %
change in demand of H product after 1% price reduction on L
product
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Conclusions New technology enables us to better understand the
link between service performance and customer behavior Estimation
challenge: limited information about the queue Combine choice
models with queuing theory Results & implications: Consumers
act as if they consider queue length, but not speed of service >
Consider splitting lines or making speed more salient Price
sensitivity negatively correlated with waiting sensitivity >
Price reductions on low priced products may generate negative
demand externalities on higher price products 27
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QUESTIONS? 28
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Queues and Traffic: Congestion Effects 29 Queue length and
transaction volume are positively correlated due to congestion
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Summary Statistics 30
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Model Estimation Details 1.Customer arrival rate ( ): store
traffic data 2.Service rate ( ): given and an initial guess of
utility model we estimate by matching the observed distribution of
queue lengths with that implied by the Erlang model. 3.Queue
length: Given and , and the initial guess of utility model we
estimate the queue length that customers faced (integrating the
uncertainty about the time when they visited the deli). 4.The
estimated queue lengths is used to estimate the probability of a
customer joining the queue. 5.The process can be repeated until
utility converges. 31
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Empirical vs Theoretical Queue distributions: 32
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Marketing and other disciplines Marketing Economics Psychology
Engineering Sociology Statistics Ethnography competition sales
force allocation consumer decisions demand forecast in-depth
consumer research word of mouth
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34 Help Vinay & Sameer Marketing Management
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35 3Cs STP+4Ps Angiomax: What price would you charge? Why Teams
Vinay and Sameers social media approach was successful? Would you
improve Starbucks service? Unilever: Should Unilever introduce a
new product in Brazil? Hulu: Ads vs No Ads? How would you promote
the Ford Ka? Molson: Why the social media campaign was not
successful?
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Purchase probability versus queue length and number of
employees 36